Optimization of rainbow trout breeding program under diverse producer preferences and genotype-byenvironment interaction

In general, there are a few rainbow trout breeding program contributing fish to many farms from different economic aspects, environments, and managements. This may cause GXE interaction. The aim of this project is to develop the optimized rainbow trout breeding program for multi trait selection and GXE interaction.

Global fish breeding distribute improved animal material to several continents to be farmed under diverse environments and for very different market conditions. Yet, fish breeding programs are at the initial phases of globalization. When establishing a global breeding program, there is a need to assess whether or not a single common breeding objective satisfies the markets across different countries and production systems. To direct genetic changes in multiple traits, selection index weights have been commonly calculated using profit functions. When animal production expands to novel areas or when a novel trait to be selected which does not have a direct economic impact, profit functions need to be replaced by the desired genetic gains method to obtain relative index weights.

Using this approach, it is possible to first derive genetic gains that the market requires and then back calculate the relative weights to be used to get these gains. Even if the breeding objective would be shared across countries, it may be challenging to develop a single fish stock that performs well across all environments. This is because there may be genotype-by-environment (GxE) interactions. GxE interaction is a phenomenon describing the possibility that different genotypes have a different sensitivity to changes in an environment.

GxE interaction has two different forms: genotype re-ranking across environments and heterogeneity of genetic variances. Re-ranking is more serious than heterogeneity of genetic variance because re-ranking means that a single genotype is not superior across all environments. The degree of re-ranking is quantified by the strength of a genetic correlation (rg) of a trait measured from different environments. When there is no re-ranking (rg = 1), selection in one environment leads to parallel genetic response in all environments, allowing an easy development of a single superior population. However, with increasing levels of re-ranking (rg < 1), it becomes more difficult to develop a population that is superior across all environments. The overall objective of this project was to develop an optimized global breeding program for rainbow trout (Oncorhynchus mykiss) in terms of a balanced breeding goal that satisfies preferences of trout producers, and maximized genetic gains across environments in the presence of GxE interaction in production traits.

Conclusions

Farmers’ preferences on breeding traits vary across farming environments and local markets. To satisfy most farmers, consensus desired genetic gains can be derived using a combination of analytical hierarchy process and weighted goal programming which can be taken into account in a global breeding strategy.

Strong GxE interaction across continents in growth traits of rainbow trout was found; however, alternative breeding scheme designs can be used to account for GxE to increase genetic gain in all environments.